Transformers Provably Learn Graph Search, Depth Exponentially Boosts Reasoning

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-11 05:00

一篇新发表在arXiv上的研究论文探讨了大型语言模型背后的架构Transformer如何学习执行图搜索，这是多步推理的一个基本方面。该研究证明，虽然单层Transformer需要相当大的规模才能实现基本的图遍历，但两层Transformer足以实现完整的深度优先搜索（DFS）能力。此外，研究表明，增加Transformer层数可以指数级地增强其前瞻视野，从而实现更复杂的推理，并且这些能力可以通过梯度下降和适当的训练数据来学习。 AI

影响提供了Transformer获得推理能力的机制性理解，可能指导未来复杂任务的模型开发。

排序理由发表在arXiv上的研究论文，详细介绍了Transformer架构在图搜索方面的理论能力。[lever_c_demoted from research: ic=1 ai=1.0]

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Transformers Provably Learn Graph Search, Depth Exponentially Boosts Reasoning

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arXiv cs.AI TIER_1 English(EN) · Xutao Ma, Somayeh Sojoudi · 2026-11-11 05:00

Transformer 可证明地学习图搜索：训练动力学与深度的指数级力量

arXiv:2611.13594v1 Announce Type: new Abstract: Large Language Models (LLMs) have achieved remarkable progress on complex reasoning tasks, yet the mechanisms by which they acquire reasoning capabilities and the efficiency of their reasoning remain poorly understood. In this paper…

本文如何被排名

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Transformer 可证明地学习图搜索：训练动力学与深度的指数级力量

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